In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 70, No. 8_Supplement ( 2010-04-15), p. 2015-2015
Abstract:
Cancer is a disease of genomic perturbations that lead to dysregulation of multiple pathways within the cellular system. While common pathways are believed to be shared within specific cancer types, the mechanisms of why particular patients respond differently to treatment is not fully understood. Current -omics studies such as The Cancer Genome Atlas (TCGA) and Stand Up To Cancer (SU2C) have attempted to address this issue by using large-scale whole-genome measurements of mRNA expression, DNA copy number, and epigenetic features. Typical analysis of these measurements relies on integrating data from multiple samples to distinguish signal from noise. However, few analytical methods allow for sample-specific differences to identify features and pathways that are significant for prognosis and clinical treatment classifications. We developed a pathway inference method called DIGMA (Directed and Integrated Graphical Modeling of Activities) to identify patient- and sample-specific pathway activities. DIGMA is capable of inferring individual gene and protein level measurements within pathways as well as overall pathway alterations specific to an individual tumor. DIGMA models each gene's “Central Dogma” within an overall pathway structure using probabilistic graphical models (PGMs). Individual -omic measurements are attached to their represented protein within the network and proteins are connected using interactions defined in curated pathway models. This representation is flexible enough to support any number of -omics measurements, which can be connected to the protein and pathway in biologically meaningful ways. Applying our method to TCGA ovarian serous carcinoma and glioblastoma multiforme samples identified pathways both significantly activated across a large majority of the cohort as well as pathways useful for classification of samples that responded well to treatment. We used random simulations to measure the significance of the pathway activities and establish a false discovery rate. These results suggest the ability to identify critical intervention points for therapeutics at a sample-specific level. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2015.
Type of Medium:
Online Resource
ISSN:
0008-5472
,
1538-7445
DOI:
10.1158/1538-7445.AM10-2015
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2010
detail.hit.zdb_id:
2036785-5
detail.hit.zdb_id:
1432-1
detail.hit.zdb_id:
410466-3
